In many industries, very large data sets are collected both in manufacturing, and in the research and development. Manufacturing processes are sometimes categorized as “batch” manufacturing processes or “continuous” manufacturing processes. In batch manufacturing processes, a series of steps with a beginning step and an ending step are performed on a set of raw and/or processed materials to produce an output. In some batch processes, processing occurs at a single workstation (e.g., a chamber or container) involving one or more process tools (e.g., process tools within the chamber or container). Examples of batch manufacturing processes include semiconductor wafer processing (e.g., processing a single wafer results in a set of chips), pharmaceutical processing (e.g., the process results in an intermediate or final output set of chemicals or drugs), or biotechnology processing (e.g., the process results in a particular biological fermentation or cell culture process). In continuous manufacturing processes, materials are manufactured, processed or produced substantially without interruption. Examples of industries employing continuous manufacturing processes are, for example, the petrochemical industry (e.g., oil and gas) or the float glass industry.
One difference between batch production and continuous production is that for continuous manufacturing processes the chemical transformations of input materials are made in substantially continuous reactions occurring in flowing streams of materials, while in batch processing, chemical transformations are performed discretely, e.g., in containers or chambers.
In the semiconductor device manufacturing industry, device manufacturers have managed to transition to more closely toleranced process and materials specifications by relying on process tool manufacturers to design better and/or faster process and hardware configurations. However, as device geometries shrink to the nanometer scale, complexity in manufacturing processes increases, and process and material specifications become more difficult to meet.
A typical process tool used in current semiconductor manufacturing can be described by a set of several thousand process variables. The variables are generally related to physical parameters of the manufacturing process and/or tools used in the manufacturing process. In some cases, of these several thousand variables, several hundred variables will be dynamic (e.g., changing in time during the manufacturing process or between manufacturing processes). The dynamic variables, for example, gas flow, gas pressure, delivered power, current, voltage, and temperature change based on, for example, a specific processing recipe, the particular step or series of steps in the overall sequence of processing steps, errors and faults that occur during the manufacturing process or changes in parameter values based on use of a particular tool or chamber (e.g., referred to as “drift”).
Similarly, in pharmaceutical and biotech production, regulatory agencies such as the U.S. Food and Drug Administration require compliance with strict specifications on the manufacturing processes to maintain high quality products with very small variation around a specified quality profile. These specifications necessitate on-line measuring of process variables and additional multidimensional sensor techniques such as, for example, process gas chromatography, near-infrared spectroscopy, and mass spectroscopy. Ideally, data measured during manufacturing processes are available for real-time analysis and/or correction to provide indications or information concerning how close the process conditions are to the process specifications and to correct for deviations from specification.
Regulatory agencies often require manufacturers to demonstrate a process is maintained within a certain “knowledge space,” where the knowledge space includes an operating region that has been explored through experimentation and/or mechanistic knowledge. For example, in the pharmaceutical and biotechnology industries, this concept is known as “Quality by Design” or “QbD.” The knowledge space can also be referred to as the “design space” and generally includes an operating region that has produced products that have been verified to meet a specified quality standard.
Process variables are frequently related to yield or response variables. The process variables can be thought of as predictors or indicators of the yield or response variables based on an underlying relationship between the variables. Data indicative of the process and yield variables are measured and stored during a manufacturing process, either for real-time or later analysis.
Generally, two categories of data are associated with a manufacturing process. One type of data, usually denoted X-type data (e.g., X data, X-variables, X sets, or observation-level data), are indicative of factors, predictors, or indicators. X-type data are used to make projections or predictions about, for example, the manufacturing process or results of the manufacturing process. Another type of data, usually denoted Y-type data (e.g., Y data, Y-variables, Y sets), are indicative of yields or responses of the manufacturing processes. X-type data and Y-type data are generally related. Often the exact relationship between the X-type data and Y-type data are uncertain or difficult or impossible to determine. The relationship can, in some instances, be approximated or modeled by various techniques (e.g., linear approximation, quadratic approximation, polynomial fitting methods, exponential or power-series relationships, multivariate techniques (such as principal component analysis or partial least squares analysis) among others). In such cases, the relationship between X-type data and Y-type data can be inferred based on observing changes to both types of data and observing the response such changes cause to the other set of data.
One way to analyze and control manufacturing processes is known as “statistical process control” (“SPC”) or “multivariate statistical process control” (“MSPC”). Generally, the statistical process control method is an open loop process in which a system provides multivariate-based monitoring of a manufacturing process to determine whether the process is operating normally. The system monitors the manufacturing process to determine whether the output product meets standards, whether the process operates consistently with past desirable operation, or other monitoring criteria. When the statistical process control system detects a deviation from normal operation, an alarm is triggered signaling the deviation to an operator or process engineer. The operator or process engineer interprets the alarm and determines the underlying cause of the deviation. The operator or engineer then manually takes corrective action to return the manufacturing process to normal operating conditions. One drawback of SPC or MSPC processes is the requirement that a process engineer or operator diagnose a problem and manually implement corrective steps.
Moreover, existing statistical process control methods are more difficult to apply to batch processes. Control over batch manufacturing processes involves monitoring a process and maintaining the process (e.g., by adjusting process parameters) along a trajectory that corresponds to a desirable result for the batch. This approach to applying multivariate control strategies to batch processes attempts to optimize a batch-level score space, and then determine the process trajectory that results in the optimized score space.